In this easy-to-understand and graph-filled article published recently by the US Centers for Disease Control and Prevention, DPM faculty member Stephen Soumerai, ScD and colleagues describe five case examples of how some of the most common biases and flawed study designs impact research on important health policies and interventions, such as comparative effectiveness of medical treatments, cost-containment policies, and health information technology.
The common biases discussed in this article include healthy user bias, volunteer selection bias, confounding by indication, social desirability bias, and history bias.
Flawed studies have dictated national flu immunization policies, influenced clinicians to falsely believe that certain sedatives could cause hip fractures in the elderly, overstated the health benefits and cost-savings of electronic health records, and exaggerated the benefits of hospital safety programs, resulting in trillions of dollars spent on interventions with few demonstrated health benefits.
Presented in the article is a useful “design hierarchy” table that ranks the ability of research designs to control for common biases. A “must read” for the public, news media, research trainees, and policymakers, this article can help one discriminate between biased and credible findings in healthcare studies.
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